A comparison of mapping strategies from DDC to CLC

Purpose: This study aims to discuss the strategies for mapping from Dewey Decimal Classification(DDC) numbers to Chinese Library Classification(CLC) numbers based on co-occurrence mapping while minimizing manual intervention.Design/methodology/approach: Several statistical tables were created based on frequency counts of the mapping relations with samples of USMARC records,which contain both DDC and CLC numbers. A manual table was created through direct mapping. In order to find reasonable mapping strategies,the mapping results were compared from three aspects including the sample size,the choice between one-to-one and one-to-multiple mapping relations,and the role of a manual mapping table.Findings: Larger sample size provides more DDC numbers in the mapping table. The statistical table including one-to-multiple DDC-CLC relations provides a higher ratio of correct matches than that including only one-to-one relations. The manual mapping table cannot produce a better result than the statistical tables. Therefore,we should make full use of statistical mapping tables and avoid the time-consuming manual mapping as much as possible.Research limitations: All the sample sizes were small. We did not consider DDC editions in our study. One-to-multiple DDC-CLC relations in the records were collected in the mapping table,but how to select one appropriate CLC number in the matching process needs to be further studied.Practical implications: The ratio of correct matches based on the statistical mapping table came up to about 90% by CLC top-level classes and 76% by the second-level classes in our study. The statistical mapping table will be improved to realize the automatic classification of e-resources and shorten the cataloging cycle significantly.Originality/value: The mapping results were investigated from different aspects in order to find suitable mapping strategies from DDC to CLC while minimizing manual intervention.The findings have facilitated the establishment of DDC-CLC mapping system for practical applications.

A 2D Mapping Method Based on Virtual Laser Scans for Indoor Robots

The indoor robots are expected to complete metric navigation tasks safely and efficiently in complex environments, which is the essential prerequisite for accomplishing other high-level operation tasks. 2 D occupancy grid maps are sufficient to support the robots in avoiding all obstacles in the environments during navigation. However, the maps based on normal laser scans only reflect a horizontal slice of the environment, which may cause the problem of some obstacles missing or misinterpreting their exact boundaries,thereby threatening the safety and efficiency of robot navigation. This paper presents a 2 D mapping method based on virtual laser scans to provide a more comprehensive representation of obstacles for indoor robot navigation. The resulting maps can accurately represent the top-down projected contours of all obstacles no matter where their vertical positions are. The virtual laser scans are initially generated from raw data of an RGB-D camera based on the filtering, projection, and polar-coordinate scanning. The scans are fed directly to the laser-based simultaneous localization and mapping(SLAM) algorithms to update the current map and robot position. Two auxiliary strategies are proposed to further improve the quality of maps by reducing the impact of the narrow field of view and the blind zone of the RGB-D camera on the observations. In this paper, the improved virtual laser generation method makes the extracted 2 D observations fit the laser-based SLAM algorithms, and two auxiliary strategies are novel ways to improve map quality. The generated maps can reflect the comprehensive obstacle information in indoor environments with good accuracy. The comparative experiments are carried out based on four simulation scenarios and three real-world scenarios to prove the effectiveness of our 2 D mapping method.